---
title: MyScale
---

This page covers how to use MyScale vector database within LangChain.
It is broken into two parts: installation and setup, and then references to specific MyScale wrappers.

With MyScale, you can manage both structured and unstructured (vectorized) data, and perform joint queries and analytics on both types of data using SQL. Plus, MyScale's cloud-native OLAP architecture, built on top of ClickHouse, enables lightning-fast data processing even on massive datasets.

## Introduction

[Overview to MyScale and High performance vector search](https://docs.myscale.com/en/overview/)

You can now register on our SaaS and [start a cluster now!](https://docs.myscale.com/en/quickstart/)

If you are also interested in how we managed to integrate SQL and vector, please refer to [this document](https://docs.myscale.com/en/vector-reference/) for further syntax reference.

We also deliver with live demo on huggingface! Please checkout our [huggingface space](https://huggingface.co/myscale)! They search millions of vector within a blink!

## Installation and Setup
- Install the Python SDK with `pip install clickhouse-connect`

### Setting up environments

There are two ways to set up parameters for myscale index.

1. Environment Variables

    Before you run the app, please set the environment variable with `export`:
        `export MYSCALE_HOST='<your-endpoints-url>' MYSCALE_PORT=<your-endpoints-port> MYSCALE_USERNAME=<your-username> MYSCALE_PASSWORD=<your-password> ...`

    You can easily find your account, password and other info on our SaaS. For details please refer to [this document](https://docs.myscale.com/en/cluster-management/)
    Every attributes under `MyScaleSettings` can be set with prefix `MYSCALE_` and is case insensitive.

2. Create `MyScaleSettings` object with parameters


    ```python
    from langchain_community.vectorstores import MyScale, MyScaleSettings
    config = MyScaleSettings(host="<your-backend-url>", port=8443, ...)
    index = MyScale(embedding_function, config)
    index.add_documents(...)
    ```

## Wrappers
supported functions:
- `add_texts`
- `add_documents`
- `from_texts`
- `from_documents`
- `similarity_search`
- `asimilarity_search`
- `similarity_search_by_vector`
- `asimilarity_search_by_vector`
- `similarity_search_with_relevance_scores`
- `delete`

### VectorStore

There exists a wrapper around MyScale database, allowing you to use it as a vectorstore,
whether for semantic search or similar example retrieval.

To import this vectorstore:
```python
from langchain_community.vectorstores import MyScale
```

For a more detailed walkthrough of the MyScale wrapper, see [this notebook](/oss/integrations/vectorstores/myscale)
